We provide an implementation of GIST in PyTorch [1]} using the NCCL distributed communication package.
Moreover, apart from the implementation for the vanilla GCN as introduced in (REF ), we also provide an implementation of GIST for GraphSAGE [2]}, another widely-used graph neural network.
The implementation for both graph neural networks is centralized, meaning that a single process serves as a central parameter server.
From this central process, the weights of the global model are maintained and partitioned to different worker processes (including itself) for independent training.
All experiments are conducted on a machine with 8 NVIDIA Tesla V100-PCIE-32G GPU, 56-core Intel(R) Xeon(R) CPU E5-2680 v4 @ 2.40GHz, and 256 GB of RAM.
